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Trends in Medical Education Research

2004· review· en· W2038192174 on OpenAlexaff
Glenn Regehr

Bibliographic record

VenueAcademic Medicine · 2004
Typereview
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsThe Wilson Centre
Fundersnot available
KeywordsCurriculumThematic analysisField (mathematics)Value (mathematics)Medical educationAccrualPublic relationsSociologyEngineering ethicsPsychologyPedagogyPolitical scienceMedicineQualitative researchSocial scienceComputer science

Abstract

fetched live from OpenAlex

The medical education community is reflecting increasingly on the role and nature of research in the field. Useful sources of data to include in these reflections are a description of the topics in which we are investing our energies, an analysis of the extent to which there is a sense of progress on these topics, and an examination of the mechanisms by which any progress has been achieved. This article presents the results of a thematic review of the medical education research literature in four key journals since the turn of the 21st century. It describes four examples of areas in which the community appears to be investing its energies: curriculum and teaching issues, skills and attitudes relevant to the structure of the profession, individual characteristics of medical students, and the evaluation of students and residents. A discussion of the recent publications in these domains highlights a distinction between thematic categories of research, in which many members of the community are working on the same topic, and programmatic lines of research, in which members of the community are working together toward the shared goal of consensual understanding. The author suggests that community-level, programmatic lines of research are necessary to build knowledge and understanding of a domain and that, in the absence of such communal effort, the value of research is limited to the uncoordinated accrual of information.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.843
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0060.009
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0030.011
Insufficient payload (model declined to judge)0.0070.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.203
GPT teacher head0.600
Teacher spread0.396 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations124
Published2004
Admission routes1
Has abstractyes

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